Summary of Provably Adaptive Average Reward Reinforcement Learning For Metric Spaces, by Avik Kar et al.
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces
by Avik Kar, Rahul Singh
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a new algorithm called ZoRL for infinite-horizon average-reward reinforcement learning (RL) that can efficiently explore and learn from complex Markov decision processes (MDPs). The algorithm discretizes the state-action space adaptively, focusing on regions that are most likely to lead to good outcomes. This allows it to achieve regret bounds of (T^{1 – d_{}^{-1}}), which is a significant improvement over previous methods that can have regret upper bounds nearly equal to O(T). The authors show through experiments that ZoRL outperforms other state-of-the-art algorithms, demonstrating the benefits of adaptivity in RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how computers can learn from complex situations without knowing everything beforehand. It creates a new way for computers to figure out what actions are best based on past experiences and the current situation. This helps computers make better decisions and improves their ability to learn. The new method is called ZoRL, and it’s more efficient than other ways computers do this kind of learning. |
Keywords
* Artificial intelligence * Reinforcement learning
